• Journal of Electrical Engineering and Technology
• /
• v.6 no.6
• /
• pp.853-866
• /
• 2011

We propose a multi-objective space search algorithm (MSSA) and introduce the identification of fuzzy inference systems based on the MSSA and information granulation (IG). The MSSA is a multi-objective optimization algorithm whose search method is associated with the analysis of the solution space. The multi-objective mechanism of MSSA is realized using a non-dominated sorting-based multi-objective strategy. In the identification of the fuzzy inference system, the MSSA is exploited to carry out parametric optimization of the fuzzy model and to achieve its structural optimization. The granulation of information is attained using the C-Means clustering algorithm. The overall optimization of fuzzy inference systems comes in the form of two identification mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and the polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by the MSSA and C-Means, whereas the parameter identification is realized via the MSSA and least squares method. The evaluation of the performance of the proposed model was conducted using three representative numerical examples such as gas furnace, NOx emission process data, and Mackey-Glass time series. The proposed model was also compared with the quality of some "conventional" fuzzy models encountered in the literature.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.30 no.1
• /
• pp.70-78
• /
• 2019

In this paper, modeling of a high-resolution multi-function radar is proposed to simulate radar performance in a war game simulator, called AddSIM. To incorporate the multi-function radar model into the AddSIM, the modeling must comprise a component-based structure consisting of physics, logics, and information blocks. Therefore, we assign the RF hardware of a RADAR as the physic block, a controller as the logics block, and the RF specifications of the RADAR as the information block. Detailed modeling of the physics and logics blocks are addressed, and data structure is also presented on an engineering level. On a multi-target engaged scenario, the performance of the multi-function radar is numerically analyzed and its validation is examined.

• Steel and Composite Structures
• /
• v.21 no.2
• /
• pp.249-266
• /
• 2016

The interest of this article lies in the proposition of using bionic method to develop a new sound absorber and analyze the efficient of this absorber in a ski cabin. Inspired by the coupling absorption structure of the skin and feather of a typical silent flying bird - owl, a bionic coupling multi-layer structure model is developed, which is composed of a micro-silt plate, porous fibrous material and a flexible micro-perforated membrane backed with airspace. The finite element simulation method with ACTRAN is applied to calculate the acoustic performance of the multi-layer absorber, the vibration modal of the ski cabin and the sound pressure level (SPL) near the skier's ears before and after pasting the absorber at the flour carpet and seats in the cabin. As expected, the SPL near the ears was significantly reduced after adding sound-absorbing material. Among them, the model 2 and model 5 showed the best sound absorption efficiency and the SPL almost reduced 5 dB. Moreover, it was most effctive for the SPL reduction with full admittance configuration at both the carpet and the seats, and the carpet contribution seems to be predominant.

• Structural Engineering and Mechanics
• /
• v.63 no.4
• /
• pp.429-438
• /
• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

• Journal of Korea Planning Association
• /
• v.54 no.5
• /
• pp.129-138
• /
• 2019

This study aims to empirically examine the relationship between building energy consumption and building and local factors in Seoul. Building energy issue is an important topic for low carbon and eco-friendly city development. Building physical, socio-economic and environmental factors effect to increasing or decreasing energy consumption. However, there are different characteristic in each area, and this kind of variable has a hierarchical structure. The multi-level model was used to consider the hierarchical structure of the variables. In this study, a multi-level model was applied to confirm the difference between areas. Spatial area is Seoul, Korea and the temporal scope is August, summer season. As the result, in Model 1 (Null Model), ICC is 0.817. This shows that the energy consumption differs by 8.174% due to factors at the Dong level. Model 2 (Random Intercept Model) suggests that building's physical factors and Average age, Household size and Land price in Dong level have significant effects on Building energy consumption. In Model 3 (Random Coefficient Model), random effect variables have intercepts and slopes to vary across groups. This study provides a perspective for policy makers that the building energy reduction policies to be applied for buildings should be differently applied on area. Furthermore, not only physical factors but also socio-economic and environmental factors are important when making energy reduction policy.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.4
• /
• pp.885-892
• /
• 2013

In the model Utilizing the TCP / IP socket communication to transmit and receive data, if the size of data is small and if data-transmission aren't frequently requested, the importance of communication speed between a server and a client isn't emphasized. But nowadays, it has emerged for large amounts of data transfer requests and frequent data transfer request. This paper propose the TCP/IP communication model that can be improved the data transfer rate in multi-core environment by changing the receiving structure of the client to receive large amounts of data and the transmission structure of the server to send large amounts of data.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.33 no.1
• /
• pp.33-40
• /
• 2010

Because sampling interval for data collection tends to be short compared with the overall processing time, in chemical process, instrumental process related tanks or furnace collected data have a significant autocorrelation. Insufficient control technique and frequent control actions cause unstable condition of the process. Traditional control charts which were developed based on iid (independently and identically distributed) among data cannot be applied on the existence of autocorrelation. Also unstable process is difficult to identity or diagnose. Because large-scale process has a lot of measurable variables and multi-step-structures among data, it is difficult to find relation between measurable variables and nonconformity. In this paper, we suggested an appicable model to diagnose the process and to find relation between measurable variables (CTQ) and nonconformity in the process having autocorrelation, unstable condition frequently, a lot of measurable variables, and multi-step-structure. And we applied this model to real process, to verify that the process engineers could easily and effectively diagnose the process and control the nonconformity.

• Journal of the Architectural Institute of Korea Structure & Construction
• /
• v.33 no.12
• /
• pp.11-18
• /
• 2017

The safety inspection cost of the construction work was based on commercial facilities classified as a single building. Therefore, it is not possible to fully reflect the characteristics of the multi-building construction project such as apartment houses. Therefore, this study suggests a reasonable estimation model that can fully reflect the characteristics of the multi-building construction project. The safety inspection cost estimation model proposed two models such as construction cost ratio method and cost plus fixed fee method. And these models were simulated by the apartment construction work and compared with the current standard. As a result, the current construction cost ratio method has shown that the safety inspection cost tends to be overestimated as the construction size increases. Therefore, the proposed model has reflected characteristics of the multi-building construction project, so that it can reasonably estimate the safety inspection cost more than the current standard.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.16 no.5
• /
• pp.404-408
• /
• 2003

The characterization for several multi-layer embedded inductors with different structures was investigated. The optimized equivalent circuit models for several test structures were obtained from HSPICE. Building blocks are modeled using Partial element equivalent circuit method. The mean and the standard deviation of model parameters were extracted and predictive modeling was performed on different test structure. From this study, the characteristic of multi-layer inductors can be predicted.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.32 no.4_1
• /
• pp.335-341
• /
• 2014

The main goal of this study is on developing a proper brand-new data of national base map and Data Based(DB) model for new information technology environments. To achieve this goal, we generated a brand-new Hybrid spatial information model which is specialized in the spatio-temporal map structure, the framework map for information integration, and the multiple-layered topology structure. The DB structure was designed to reflect the change of objections by adding a new dimension of 'time' in the spartial information, while the infrastructure was able to connect/converge with other information by giving the unique ID and multi-scale fusion map structure. Furthermore, the topology and multi visualization structure, including indoor and basement information, were designed to overcome limitations of expressing in 2 dimension map. The result from the performance test, which was based on the Hybrid spatial information model, confirms the possibility in advanced national base map and conducted DB model through implementing various information and spatiotemporal connections.